Overview

Dataset statistics

Number of variables15
Number of observations40000
Missing cells32187
Missing cells (%)5.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 MiB
Average record size in memory120.0 B

Variable types

Numeric12
Categorical3

Alerts

energy is highly correlated with loudnessHigh correlation
loudness is highly correlated with energyHigh correlation
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly correlated with acousticness and 1 other fieldsHigh correlation
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly correlated with acousticness and 1 other fieldsHigh correlation
song_duration_ms has 4101 (10.3%) missing values Missing
acousticness has 3992 (10.0%) missing values Missing
danceability has 4026 (10.1%) missing values Missing
energy has 3975 (9.9%) missing values Missing
instrumentalness has 3985 (10.0%) missing values Missing
key has 4065 (10.2%) missing values Missing
liveness has 4086 (10.2%) missing values Missing
loudness has 3957 (9.9%) missing values Missing
id is uniformly distributed Uniform
id has unique values Unique
key has 5175 (12.9%) zeros Zeros

Reproduction

Analysis started2022-01-28 17:49:29.316453
Analysis finished2022-01-28 17:50:05.102063
Duration35.79 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct40000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19999.5
Minimum0
Maximum39999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-01-28T22:50:05.198586image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1999.95
Q19999.75
median19999.5
Q329999.25
95-th percentile37999.05
Maximum39999
Range39999
Interquartile range (IQR)19999.5

Descriptive statistics

Standard deviation11547.14972
Coefficient of variation (CV)0.5773719203
Kurtosis-1.2
Mean19999.5
Median Absolute Deviation (MAD)10000
Skewness0
Sum799980000
Variance133336666.7
MonotonicityStrictly increasing
2022-01-28T22:50:05.349448image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
354921
 
< 0.1%
252411
 
< 0.1%
313861
 
< 0.1%
293391
 
< 0.1%
191001
 
< 0.1%
170531
 
< 0.1%
231981
 
< 0.1%
211511
 
< 0.1%
334451
 
< 0.1%
Other values (39990)39990
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
399991
< 0.1%
399981
< 0.1%
399971
< 0.1%
399961
< 0.1%
399951
< 0.1%
399941
< 0.1%
399931
< 0.1%
399921
< 0.1%
399911
< 0.1%
399901
< 0.1%

song_duration_ms
Real number (ℝ≥0)

MISSING

Distinct31586
Distinct (%)88.0%
Missing4101
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean193165.8476
Minimum25658
Maximum491671
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-01-28T22:50:05.497336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum25658
5-th percentile127469.5
Q1166254.5
median186660
Q3215116
95-th percentile277074.1
Maximum491671
Range466013
Interquartile range (IQR)48861.5

Descriptive statistics

Standard deviation45822.12768
Coefficient of variation (CV)0.2372165072
Kurtosis1.654184865
Mean193165.8476
Median Absolute Deviation (MAD)23432
Skewness0.632931575
Sum6934460762
Variance2099667385
MonotonicityNot monotonic
2022-01-28T22:50:05.645545image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1620486
 
< 0.1%
1603695
 
< 0.1%
1745465
 
< 0.1%
1915214
 
< 0.1%
1666764
 
< 0.1%
1667474
 
< 0.1%
1835344
 
< 0.1%
1826274
 
< 0.1%
1878894
 
< 0.1%
1728884
 
< 0.1%
Other values (31576)35855
89.6%
(Missing)4101
 
10.3%
ValueCountFrequency (%)
256581
< 0.1%
339941
< 0.1%
348161
< 0.1%
388611
< 0.1%
421091
< 0.1%
428561
< 0.1%
439711
< 0.1%
440081
< 0.1%
448341
< 0.1%
448391
< 0.1%
ValueCountFrequency (%)
4916711
< 0.1%
4705771
< 0.1%
4656361
< 0.1%
4449711
< 0.1%
4410461
< 0.1%
4281561
< 0.1%
4278881
< 0.1%
4241441
< 0.1%
4241351
< 0.1%
4227711
< 0.1%

acousticness
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct36004
Distinct (%)> 99.9%
Missing3992
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean0.2764044304
Minimum-0.01355115526
Maximum1.065284361
Zeros0
Zeros (%)0.0%
Negative372
Negative (%)0.9%
Memory size312.6 KiB
2022-01-28T22:50:05.794060image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.01355115526
5-th percentile0.006948470296
Q10.0396183645
median0.1405323489
Q30.4824987531
95-th percentile0.9144326981
Maximum1.065284361
Range1.078835516
Interquartile range (IQR)0.4428803886

Descriptive statistics

Standard deviation0.2979279293
Coefficient of variation (CV)1.077869587
Kurtosis-0.3117888002
Mean0.2764044304
Median Absolute Deviation (MAD)0.1257318182
Skewness1.004194448
Sum9952.770729
Variance0.08876105104
MonotonicityNot monotonic
2022-01-28T22:50:05.937700image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.12986487052
 
< 0.1%
0.26862477672
 
< 0.1%
0.012316337092
 
< 0.1%
0.023058241372
 
< 0.1%
0.12990481411
 
< 0.1%
0.16035113931
 
< 0.1%
0.62205002991
 
< 0.1%
0.13690813551
 
< 0.1%
0.37127574521
 
< 0.1%
0.85829994131
 
< 0.1%
Other values (35994)35994
90.0%
(Missing)3992
 
10.0%
ValueCountFrequency (%)
-0.013551155261
< 0.1%
-0.012245462061
< 0.1%
-0.012156104951
< 0.1%
-0.010999938061
< 0.1%
-0.01084703411
< 0.1%
-0.010766391261
< 0.1%
-0.010352106741
< 0.1%
-0.0096921638251
< 0.1%
-0.0096760178581
< 0.1%
-0.0095726758091
< 0.1%
ValueCountFrequency (%)
1.0652843611
< 0.1%
1.0625157381
< 0.1%
1.0624626971
< 0.1%
1.0511640711
< 0.1%
1.0455373831
< 0.1%
1.0436914711
< 0.1%
1.041086871
< 0.1%
1.0403366231
< 0.1%
1.0374889451
< 0.1%
1.0367453771
< 0.1%

danceability
Real number (ℝ≥0)

MISSING

Distinct35972
Distinct (%)> 99.9%
Missing4026
Missing (%)10.1%
Infinite0
Infinite (%)0.0%
Mean0.5709511548
Minimum0.04396067634
Maximum0.9571308047
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-01-28T22:50:06.078205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.04396067634
5-th percentile0.2329293652
Q10.4247604608
median0.6082338356
Q30.7184637554
95-th percentile0.839682299
Maximum0.9571308047
Range0.9131701284
Interquartile range (IQR)0.2937032947

Descriptive statistics

Standard deviation0.1900104818
Coefficient of variation (CV)0.3327963875
Kurtosis-0.8125678234
Mean0.5709511548
Median Absolute Deviation (MAD)0.137040659
Skewness-0.3983857384
Sum20539.39684
Variance0.03610398319
MonotonicityNot monotonic
2022-01-28T22:50:06.228170image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.34844173562
 
< 0.1%
0.48227267412
 
< 0.1%
0.72955947281
 
< 0.1%
0.154999431
 
< 0.1%
0.18247232051
 
< 0.1%
0.450019771
 
< 0.1%
0.63355711361
 
< 0.1%
0.76416289361
 
< 0.1%
0.49167923221
 
< 0.1%
0.69221945351
 
< 0.1%
Other values (35962)35962
89.9%
(Missing)4026
 
10.1%
ValueCountFrequency (%)
0.043960676341
< 0.1%
0.049807075641
< 0.1%
0.053681992311
< 0.1%
0.056268720241
< 0.1%
0.057079687851
< 0.1%
0.059137497521
< 0.1%
0.063838026571
< 0.1%
0.068134172921
< 0.1%
0.069431610991
< 0.1%
0.072968156521
< 0.1%
ValueCountFrequency (%)
0.95713080471
< 0.1%
0.95494465591
< 0.1%
0.9503584011
< 0.1%
0.94773225661
< 0.1%
0.9406807881
< 0.1%
0.93952293941
< 0.1%
0.9352423861
< 0.1%
0.93312804291
< 0.1%
0.93311000891
< 0.1%
0.93292435811
< 0.1%

energy
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct36021
Distinct (%)> 99.9%
Missing3975
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean0.6839319477
Minimum-0.001682200414
Maximum1.039741305
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size312.6 KiB
2022-01-28T22:50:06.582186image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.001682200414
5-th percentile0.2827326087
Q10.5392758057
median0.7044533183
Q30.8705031183
95-th percentile0.9606421252
Maximum1.039741305
Range1.041423505
Interquartile range (IQR)0.3312273125

Descriptive statistics

Standard deviation0.2126620002
Coefficient of variation (CV)0.3109402929
Kurtosis-0.4092486749
Mean0.6839319477
Median Absolute Deviation (MAD)0.1657402625
Skewness-0.5828321649
Sum24638.64842
Variance0.04522512632
MonotonicityNot monotonic
2022-01-28T22:50:06.742567image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.83482834062
 
< 0.1%
0.79880724642
 
< 0.1%
0.8947917152
 
< 0.1%
0.85314118912
 
< 0.1%
0.68597239221
 
< 0.1%
0.85250370411
 
< 0.1%
0.63907538651
 
< 0.1%
0.62967816481
 
< 0.1%
0.86428544421
 
< 0.1%
0.66264644671
 
< 0.1%
Other values (36011)36011
90.0%
(Missing)3975
 
9.9%
ValueCountFrequency (%)
-0.0016822004141
< 0.1%
0.0023224251731
< 0.1%
0.0071134709351
< 0.1%
0.016732061311
< 0.1%
0.018046402851
< 0.1%
0.018351671091
< 0.1%
0.021394285611
< 0.1%
0.022756680411
< 0.1%
0.023821172051
< 0.1%
0.027284869121
< 0.1%
ValueCountFrequency (%)
1.0397413051
< 0.1%
1.0342805881
< 0.1%
1.0337466551
< 0.1%
1.0334342471
< 0.1%
1.0332394241
< 0.1%
1.0304094131
< 0.1%
1.0297993641
< 0.1%
1.0286322241
< 0.1%
1.0283425851
< 0.1%
1.0280605511
< 0.1%

instrumentalness
Real number (ℝ)

MISSING

Distinct35999
Distinct (%)> 99.9%
Missing3985
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean0.03652675599
Minimum-0.004398050997
Maximum1.075414681
Zeros0
Zeros (%)0.0%
Negative3216
Negative (%)8.0%
Memory size312.6 KiB
2022-01-28T22:50:06.896685image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.004398050997
5-th percentile-0.0004201654271
Q10.0009414648076
median0.00197447663
Q30.003225298806
95-th percentile0.1489803127
Maximum1.075414681
Range1.079812732
Interquartile range (IQR)0.002283833998

Descriptive statistics

Standard deviation0.1500239025
Coefficient of variation (CV)4.107233135
Kurtosis24.17958059
Mean0.03652675599
Median Absolute Deviation (MAD)0.001120578988
Skewness4.947630572
Sum1315.511117
Variance0.02250717132
MonotonicityNot monotonic
2022-01-28T22:50:07.056187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0018504257362
 
< 0.1%
0.0018054573032
 
< 0.1%
0.0012916020622
 
< 0.1%
0.0012720465792
 
< 0.1%
0.0023800061892
 
< 0.1%
0.0015315137642
 
< 0.1%
0.0017187441222
 
< 0.1%
0.0023156290832
 
< 0.1%
0.00048184010542
 
< 0.1%
0.0021951563812
 
< 0.1%
Other values (35989)35995
90.0%
(Missing)3985
 
10.0%
ValueCountFrequency (%)
-0.0043980509971
< 0.1%
-0.0038541022111
< 0.1%
-0.0036410694231
< 0.1%
-0.0032578757621
< 0.1%
-0.0032261645621
< 0.1%
-0.0030877031731
< 0.1%
-0.0030171057481
< 0.1%
-0.0029771299031
< 0.1%
-0.0029500459531
< 0.1%
-0.0028399811011
< 0.1%
ValueCountFrequency (%)
1.0754146811
< 0.1%
1.0648473781
< 0.1%
1.0414794791
< 0.1%
1.0369281361
< 0.1%
1.0302626821
< 0.1%
1.0293002191
< 0.1%
1.0223041421
< 0.1%
1.0155688361
< 0.1%
1.0128398491
< 0.1%
1.0123543661
< 0.1%

key
Real number (ℝ≥0)

MISSING
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing4065
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean5.042604703
Minimum0
Maximum11
Zeros5175
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-01-28T22:50:07.178539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.372727801
Coefficient of variation (CV)0.6688463601
Kurtosis-1.274787585
Mean5.042604703
Median Absolute Deviation (MAD)3
Skewness-0.07530404301
Sum181206
Variance11.37529282
MonotonicityNot monotonic
2022-01-28T22:50:07.273620image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
05175
12.9%
64949
12.4%
104302
10.8%
24080
10.2%
83028
7.6%
52985
7.5%
92881
7.2%
72690
6.7%
42239
5.6%
12143
5.4%
Other values (2)1463
 
3.7%
(Missing)4065
10.2%
ValueCountFrequency (%)
05175
12.9%
12143
5.4%
24080
10.2%
31346
 
3.4%
42239
5.6%
52985
7.5%
64949
12.4%
72690
6.7%
83028
7.6%
92881
7.2%
ValueCountFrequency (%)
11117
 
0.3%
104302
10.8%
92881
7.2%
83028
7.6%
72690
6.7%
64949
12.4%
52985
7.5%
42239
5.6%
31346
 
3.4%
24080
10.2%

liveness
Real number (ℝ≥0)

MISSING

Distinct35911
Distinct (%)> 99.9%
Missing4086
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean0.1985136912
Minimum0.02784311302
Maximum1.065298031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-01-28T22:50:07.409952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.02784311302
5-th percentile0.07990015233
Q10.1117963607
median0.1359451827
Q30.2128416344
95-th percentile0.5817990771
Maximum1.065298031
Range1.037454918
Interquartile range (IQR)0.1010452737

Descriptive statistics

Standard deviation0.1516699134
Coefficient of variation (CV)0.7640274707
Kurtosis4.411722538
Mean0.1985136912
Median Absolute Deviation (MAD)0.03733661592
Skewness2.165545688
Sum7129.420707
Variance0.02300376264
MonotonicityNot monotonic
2022-01-28T22:50:07.600070image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13646559572
 
< 0.1%
0.11927528272
 
< 0.1%
0.13177630642
 
< 0.1%
0.59037443561
 
< 0.1%
0.22073293011
 
< 0.1%
0.10436822971
 
< 0.1%
0.19975127691
 
< 0.1%
0.20128476521
 
< 0.1%
0.1203591661
 
< 0.1%
0.46602457761
 
< 0.1%
Other values (35901)35901
89.8%
(Missing)4086
 
10.2%
ValueCountFrequency (%)
0.027843113021
< 0.1%
0.030298142421
< 0.1%
0.031251658411
< 0.1%
0.032306469591
< 0.1%
0.032781671641
< 0.1%
0.034524952191
< 0.1%
0.034597043881
< 0.1%
0.035280125241
< 0.1%
0.036110651731
< 0.1%
0.03691938231
< 0.1%
ValueCountFrequency (%)
1.0652980311
< 0.1%
1.02861
< 0.1%
0.99022351131
< 0.1%
0.98041072461
< 0.1%
0.97963245091
< 0.1%
0.97848639511
< 0.1%
0.97544427371
< 0.1%
0.97036754781
< 0.1%
0.95914900811
< 0.1%
0.95440003241
< 0.1%

loudness
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct36038
Distinct (%)> 99.9%
Missing3957
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean-7.407596208
Minimum-32.11791085
Maximum-0.8773458492
Zeros0
Zeros (%)0.0%
Negative36043
Negative (%)90.1%
Memory size312.6 KiB
2022-01-28T22:50:07.746927image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-32.11791085
5-th percentile-14.81626983
Q1-9.578139218
median-6.345412615
Q3-4.620711264
95-th percentile-3.029093555
Maximum-0.8773458492
Range31.240565
Interquartile range (IQR)4.957427954

Descriptive statistics

Standard deviation3.877197599
Coefficient of variation (CV)-0.5234083353
Kurtosis2.79043824
Mean-7.407596208
Median Absolute Deviation (MAD)2.118409249
Skewness-1.430100333
Sum-266991.9901
Variance15.03266123
MonotonicityNot monotonic
2022-01-28T22:50:07.883106image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.8249332322
 
< 0.1%
-2.7037615732
 
< 0.1%
-9.9300807842
 
< 0.1%
-11.109011412
 
< 0.1%
-5.7542751522
 
< 0.1%
-3.9868299371
 
< 0.1%
-12.31529421
 
< 0.1%
-13.061026491
 
< 0.1%
-9.7434398821
 
< 0.1%
-21.762514321
 
< 0.1%
Other values (36028)36028
90.1%
(Missing)3957
 
9.9%
ValueCountFrequency (%)
-32.117910851
< 0.1%
-30.170398531
< 0.1%
-30.091074261
< 0.1%
-30.088842361
< 0.1%
-30.012386221
< 0.1%
-29.5970871
< 0.1%
-29.361234091
< 0.1%
-29.19991071
< 0.1%
-29.175447781
< 0.1%
-29.043994341
< 0.1%
ValueCountFrequency (%)
-0.87734584921
< 0.1%
-1.0732166391
< 0.1%
-1.0994580211
< 0.1%
-1.1258535731
< 0.1%
-1.1735709271
< 0.1%
-1.2040696091
< 0.1%
-1.2141767971
< 0.1%
-1.2184802881
< 0.1%
-1.2267539251
< 0.1%
-1.2357160261
< 0.1%

audio_mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
0
27154 
1
12846 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
027154
67.9%
112846
32.1%

Length

2022-01-28T22:50:08.024048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T22:50:08.105658image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
027154
67.9%
112846
32.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

speechiness
Real number (ℝ≥0)

Distinct39999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09410654997
Minimum0.01506465912
Maximum0.5607483509
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-01-28T22:50:08.190086image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.01506465912
5-th percentile0.02929199988
Q10.03850013208
median0.05588077326
Q30.1188421391
95-th percentile0.2765419244
Maximum0.5607483509
Range0.5456836918
Interquartile range (IQR)0.08034200698

Descriptive statistics

Standard deviation0.08359080853
Coefficient of variation (CV)0.8882570719
Kurtosis2.47562356
Mean0.09410654997
Median Absolute Deviation (MAD)0.02167172596
Skewness1.72012054
Sum3764.261999
Variance0.006987423271
MonotonicityNot monotonic
2022-01-28T22:50:08.320026image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03332368772
 
< 0.1%
0.10086473191
 
< 0.1%
0.070351142191
 
< 0.1%
0.036815589061
 
< 0.1%
0.34074351761
 
< 0.1%
0.17478900631
 
< 0.1%
0.026505330381
 
< 0.1%
0.17373244191
 
< 0.1%
0.035492343421
 
< 0.1%
0.26147466421
 
< 0.1%
Other values (39989)39989
> 99.9%
ValueCountFrequency (%)
0.015064659121
< 0.1%
0.015153370831
< 0.1%
0.015647635011
< 0.1%
0.018333543311
< 0.1%
0.018534984141
< 0.1%
0.018792620411
< 0.1%
0.018881818121
< 0.1%
0.018882516541
< 0.1%
0.0190103721
< 0.1%
0.019036016991
< 0.1%
ValueCountFrequency (%)
0.56074835091
< 0.1%
0.55101416971
< 0.1%
0.54779896811
< 0.1%
0.53325671971
< 0.1%
0.53062109071
< 0.1%
0.52749525141
< 0.1%
0.51901222491
< 0.1%
0.51587575561
< 0.1%
0.51491166251
< 0.1%
0.50830948751
< 0.1%

tempo
Real number (ℝ≥0)

Distinct39993
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.5628149
Minimum62.05577908
Maximum219.1635782
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-01-28T22:50:08.476313image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum62.05577908
5-th percentile81.3193984
Q196.99530887
median113.7959594
Q3128.5173834
95-th percentile170.9800477
Maximum219.1635782
Range157.1077991
Interquartile range (IQR)31.52207449

Descriptive statistics

Standard deviation26.16791062
Coefficient of variation (CV)0.2244962139
Kurtosis0.1752832209
Mean116.5628149
Median Absolute Deviation (MAD)16.00719581
Skewness0.7641500817
Sum4662512.595
Variance684.7595463
MonotonicityNot monotonic
2022-01-28T22:50:08.608978image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.095949182
 
< 0.1%
90.561987712
 
< 0.1%
95.069235822
 
< 0.1%
88.182389542
 
< 0.1%
103.19007732
 
< 0.1%
127.08650762
 
< 0.1%
111.8275712
 
< 0.1%
139.2345791
 
< 0.1%
91.920837951
 
< 0.1%
106.67593081
 
< 0.1%
Other values (39983)39983
> 99.9%
ValueCountFrequency (%)
62.055779081
< 0.1%
62.889001611
< 0.1%
63.248335691
< 0.1%
63.457446691
< 0.1%
63.47887651
< 0.1%
63.479412561
< 0.1%
63.530433111
< 0.1%
63.757481851
< 0.1%
63.977201821
< 0.1%
63.994738781
< 0.1%
ValueCountFrequency (%)
219.16357821
< 0.1%
218.63743891
< 0.1%
215.86136771
< 0.1%
212.54354961
< 0.1%
212.42548191
< 0.1%
211.78647621
< 0.1%
211.57938051
< 0.1%
211.16127021
< 0.1%
211.09140661
< 0.1%
210.4808381
< 0.1%

time_signature
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
3
23358 
4
15919 
2
 
530
5
 
193

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row3
4th row3
5th row4

Common Values

ValueCountFrequency (%)
323358
58.4%
415919
39.8%
2530
 
1.3%
5193
 
0.5%

Length

2022-01-28T22:50:08.743419image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T22:50:08.825202image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
323358
58.4%
415919
39.8%
2530
 
1.3%
5193
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

audio_valence
Real number (ℝ≥0)

Distinct39998
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5806448561
Minimum0.01339785461
Maximum1.022557576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2022-01-28T22:50:08.926198image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.01339785461
5-th percentile0.1591460592
Q10.3986694172
median0.5988269955
Q30.7596351387
95-th percentile0.9474466885
Maximum1.022557576
Range1.009159721
Interquartile range (IQR)0.3609657215

Descriptive statistics

Standard deviation0.2373507637
Coefficient of variation (CV)0.4087709745
Kurtosis-0.858197458
Mean0.5806448561
Median Absolute Deviation (MAD)0.1804709655
Skewness-0.1810057441
Sum23225.79424
Variance0.05633538502
MonotonicityNot monotonic
2022-01-28T22:50:09.072308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.74571518732
 
< 0.1%
0.68590688492
 
< 0.1%
0.35687812771
 
< 0.1%
0.86431916721
 
< 0.1%
0.20793481751
 
< 0.1%
0.42936155671
 
< 0.1%
0.14753673751
 
< 0.1%
0.59105074621
 
< 0.1%
0.81890603311
 
< 0.1%
0.41272078681
 
< 0.1%
Other values (39988)39988
> 99.9%
ValueCountFrequency (%)
0.013397854611
< 0.1%
0.017351387021
< 0.1%
0.020050643571
< 0.1%
0.02394958431
< 0.1%
0.027657343591
< 0.1%
0.029565538661
< 0.1%
0.030384835331
< 0.1%
0.031417705411
< 0.1%
0.03155597351
< 0.1%
0.032900556331
< 0.1%
ValueCountFrequency (%)
1.0225575761
< 0.1%
1.0222826851
< 0.1%
1.0185709381
< 0.1%
1.0183015921
< 0.1%
1.0176927641
< 0.1%
1.0152003331
< 0.1%
1.0134842161
< 0.1%
1.0134455191
< 0.1%
1.0130554871
< 0.1%
1.0129285481
< 0.1%

song_popularity
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
0
25424 
1
14576 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025424
63.6%
114576
36.4%

Length

2022-01-28T22:50:09.222656image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-28T22:50:09.303536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
025424
63.6%
114576
36.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-01-28T22:50:02.315492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:39.451093image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:42.809243image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:44.392356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:46.098483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:48.152225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:50.478406image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:53.279574image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:55.312397image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:56.780839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:58.833147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:50:00.762643image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:50:02.450404image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:39.604637image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:42.949583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:44.529343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:46.245603image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:48.432809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:50.665546image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:53.512318image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:55.453018image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:56.909952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:58.973726image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:50:00.905119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:50:02.563159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:39.740079image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:43.065915image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:44.649667image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:46.372140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:48.625125image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:50.846730image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:53.699121image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:55.569944image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:57.028688image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:59.100463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:50:01.027790image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:50:02.685903image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:41.385355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:43.188274image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:44.774218image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:46.496253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:48.777989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:51.044179image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:53.896232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:55.685492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:57.261586image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:59.256195image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:50:01.154867image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:50:02.800389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:41.631671image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:43.415390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:44.897547image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:46.758475image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:49.005378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:51.247145image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:54.098907image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:55.801191image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:57.490190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:59.400247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:50:01.280300image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:50:02.921493image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:41.829701image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:43.534715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:45.039671image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:46.879981image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:49.227011image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:51.497258image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:54.262592image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:55.915818image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:57.781000image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:59.535577image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:50:01.412207image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:50:03.052380image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:41.965533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:43.653479image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:45.198984image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:47.013263image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:49.381393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:51.640309image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:54.408006image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:56.033364image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:57.964236image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:59.669250image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:50:01.540940image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:50:03.166496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:42.085884image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:43.770946image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:45.339913image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:47.142051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-28T22:49:49.527527image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-28T22:50:09.596727image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-28T22:50:09.810285image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-28T22:50:09.997487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-28T22:50:10.130561image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
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The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
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The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idsong_duration_msacousticnessdanceabilityenergyinstrumentalnesskeylivenessloudnessaudio_modespeechinesstempotime_signatureaudio_valencesong_popularity
00212990.00.6422860.8565200.7070730.00200110.0NaN-5.61908800.082570158.38623640.7346420
11NaN0.0548660.7332890.8355450.0009968.00.436428-5.23696510.127358102.75298830.7115311
22193213.0NaN0.1883870.783524-0.0026945.00.170499-4.95175900.052282178.68579130.4255360
33249893.00.4886600.5852340.5526850.0006080.00.094805-7.89369400.035618128.71563030.4535970
44165969.00.493017NaN0.7409820.00203310.00.094891-2.68409500.050746121.92815740.7413110
55188891.00.0356550.8259190.804528-0.0000054.00.120758-6.12292600.039012115.67912840.7094080
66161061.00.0817430.6735880.8801810.0003270.00.535411-2.90960710.03090298.04620540.9827290
77196202.00.2597470.8132140.5543850.0003908.00.276580-7.79423700.207067158.62676430.6629871
88169660.0NaN0.6532630.9170340.0017480.0NaN-4.42208900.031608122.38239830.2976831
99167245.00.0196170.5952350.8200390.7618845.00.181098-5.15429300.054493110.52482440.5354530

Last rows

idsong_duration_msacousticnessdanceabilityenergyinstrumentalnesskeylivenessloudnessaudio_modespeechinesstempotime_signatureaudio_valencesong_popularity
3999039990161037.0NaN0.1808150.3534950.0060772.00.123413-16.85268500.03419379.63002630.4374971
3999139991171968.00.139985NaN0.6970600.0001270.00.216089-7.56000910.16993986.46999230.7224200
3999239992402730.00.0060450.650712NaN0.00269410.00.215647-2.61999400.059303116.79153140.5258060
3999339993147151.00.1065880.6164300.9230220.0033276.00.066085-4.01703400.185710118.63268340.3933281
3999439994158043.0NaN0.2561820.6991620.00275710.00.385996-9.91771700.042718103.43178540.2088921
3999539995237799.0NaN0.7489420.5112340.0029130.00.173803-8.85367300.078060168.26292430.1781590
3999639996191119.00.0674880.6728300.8896850.0012255.00.122924-7.79899300.188607110.68454430.7906260
3999739997160879.00.8774310.4090650.292671NaN10.00.110664-15.37858500.03129499.55607430.1779471
3999839998193918.0NaN0.365738NaN0.0003391.00.356308-4.66197710.054096139.85738430.7729780
3999939999196475.00.0071160.3545850.9948830.0021911.00.200900-4.87524900.080549101.97494930.5885490